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The paper reviews community detection methods used besides other partition algorithms for power grid partitioning. The power grid is a complex network. A complex network forms a community structure. There are many edges between community nodes and relatively few of those connecting the vertices of different communities. Community detection is an optimization problem. Its goal is to detect communities that belong to a network or graph in which nodes of a particular community in the same community share properties of the same characteristics or functional relationships. Power grids use adaptable community detection algorithms of various suitability rates used in power grid partitioning. The paper presents types and adaptations of various community detection algorithms used in power grid partitioning. [ABSTRACT FROM AUTHOR]

The performance of the evolutionary algorithms is assessed by comparing them with the existing algorithms. Comparison of the evolutionary algorithms in the literature plays a key role as it illustrates which algorithms are better. It is a common practice to simply take the results from the papers of the algorithms you are comparing your proposed algorithm to. However, it is not guaranteed that the experiments are performed in accordance with the guidelines for a fair comparison. Since a correct execution of an experiment is crucial to be able to state with a certainty the superiority of evolutionary algorithms, it is recommended that the experiment is repeated. This, however, can be a challenge if the source code of the algorithm is not available or it needs to be implemented in another programming language. Such approach can lead to misleading results due to the unintentional mistakes made in implementing the algorithm. Another factor that is often overlooked are the differences between the programming languages in which the algorithm is implemented. The problem is highlighted by analyzing and comparing the performance of the GWO algorithm implemented in six programming languages. [ABSTRACT FROM AUTHOR]